Access Alliance, a community health centre based in Toronto, is working to address health equity at many levels. In their QIP, they described their approach to addressing health equity by collecting and using sociodemographic data within their organization, as part of a regional collaborative, and also through an initiative aimed at building capacity for health equity across the province.
Health Quality Ontario had a chance to speak with Akm Alamgir, Manager, Quality and Accountability Systems and Cliff Ledwos, Director, Primary Health Care to find out more.
Could you tell us about your organization’s approach to collecting sociodemographic data? How do you use this data?
Our mission at Access Alliance is to provide services and addresses system inequities to improve health outcomes for the most vulnerable immigrants, refugees, and their communities. Since 2014, we have participated in the Measuring Health Equity project led by Mount Sinai Hospital, where we collect data on eight health equity demographic questions in our client onboarding forms.
We always set a critical question upfront for the data we collect: How will the findings be used to improve the programs or services? We use demographic data to help inform quality improvement projects to improve health care access/delivery and health outcomes among our client groups.
Our approach involves two steps: first, we break down aggregate (population-level) data into subpopulations or by different demographic indicators ( i.e. disaggregated data); then we explore how the different indicators link and intersect to produce multiple types of marginalization.
For example newcomer women who are precariously employed have different needs and face different risk factors than Canadian born men who are precariously employed. This approach to looking at several intersecting identities is called intersectional analysis, and can reveal target areas for quality improvement work.
As an example, we have conducted a “No-show (Missed Opportunity in Care)’’ study to better understand the client and institutional level factors that contribute to no-show appointments at Access Alliance. From this point, we can then explore the potential reasons or barriers preventing clients from coming to appointments, and create an evidence-informed quality improvement strategy targeting key client groups in order to improve access.
You mentioned you are participating in a collaborative with other Community Health Centres to develop an equity-focused QIP indicator. Could you tell us about that project?
We participate in a collaborative between six community health centres called the West End Quality Improvement Collaborative.This collaborative is made up of Davenport Perth CHC, Unison Health and Community Services, Parkdale CHC, Queen West CHC, Four Villages CHC, and Access Alliance Multicultural Health and Community Services. We believed that we could increase the impact of our efforts if we all worked together to improve on the same indicator.
We found that it was actually fairly easy to identify what we wanted to work on by looking at the data. We chose to work on cancer screening because we believed that we could make some visible progress on this as a group. We also incorporated an equity lens into our approach. Our goal was to understand barriers to cancer screening, improve clients’ care experience, and increase the number of screened clients.
One of the great outcomes of this collaborative was how we were able to share our learnings among the agencies. One agency would learn something and they would share this with the other five. If one agency stumbled, they could ask for advice from the others as well.
Could you tell us about the work you’ve done at the provincial level supporting the use of data to address health equity?
We’ve worked on the Health Equity Indicators Project in collaboration with the Toronto Central LHIN, Alliance for Healthier Communities, and the Ontario Council of Agencies Serving Immigrants, among other partners.
Basically, the aim was to develop common health equity frameworks and indicators that, together with efficiency and effectiveness indicators, improve program quality, reduce inequity, and demonstrate best value for resource investments.
Seven community health centres have participated in this project to date. These organizations received training and coaching to integrate equity indicators in all phases of program and organizational planning, monitoring, benchmarking and reporting.
At the beginning of this project, we generated a health equity profile of each participating organization as a ‘pre-test’ evaluation. These profiles included information on the following questions:
- What was their current state with regard to their collection of indicators?
- Did they have a data management coordinator?
- Who collects, uses, and analyzes the data?
- Is their strategic planning aligned with equity?
- Is their operational planning aimed at reducing equity gaps?
We found that the participating community health centres are at different levels with regard to use of indicators and collection and use of data.
In addition to understanding the current state through these health equity profiles, we also needed to understand the actions needed for improvement. We found that different organizations had different understandings of what equity means, so we had to be sure everyone had a common understanding. Another common point was that organizations were not sure how to use sociodemographic data in a meaningful way.
As part of this project, we developed three training modules and honed them based on feedback. These are available on the Alliance for Healthier Communities website.
At the end of the program, we did a post-test evaluation and identified two main accomplishments of the program:
- After participating, people feel that they can connect health equity and quality, and are able to use data to do this.
- Alliance for Healthier Communities has created a community of practice on health equity and evaluation.
Overall, this project has led to increased capacity on health equity-focused projects.
What advice would you give to other organizations who are just starting out using sociodemographic data for quality improvement?
One learning from the recently completed Health Equity Indicators Project is that organizations might be at very different levels with regard to their capacity to work with data. We suggest that organizations should:
- Prepare an individual Quality Improvement Framework that converges the accountability and quality congruently
- Create a “current state” organization profile for quality data operations, which will include:
- Current status of collecting demographic data, i.e., what data they are collecting now
- Capacity and resources of the organization to plan, collect, analyze, and interpret quality data
- Find available budget and policy for building capacity and supports
- Create a future state profile map
- Partner with similar agencies in the region
- Create a regional platform for the QIPs
- Create a community of practice for integrated quality improvement wor
You may also be interested in:
Quorum’s Indicators & Change Ideas page. Find more information on QIP indicators and related change ideas.